Hybrid quantum-classical approaches from the family of variational quantum algorithms ([1]}) were originally introduced in the chemistry context ([2]}) and have recently been investigated for solving various machine learning tasks on NISQ devices, e.g., classification ([3]}, [4]}, [5]}), graph embedding ([6]}), or approximation of the deep Q-value function in reinforcement learning settings ([7]})In the academic literature, variational quantum algorithms are also mentioned as quantum neural networks ([4]}), parametrized quantum circuits ([9]}), or qunatum circuit learning ([10]})..
The key part of these algorithms is a variational quantum circuit parametrized by a scalable parameter vector, which is updated in the learning phase by classically minimizing the cost function of interest ([9]}, [10]}).
To perform a machine learning task with a quantum circuit, the data serving as input must be represented as a quantum state.
Since the data is usually available in a classical form, encoding is a crucial part of the variational quantum algorithms.
